1. Technical Field
The present disclosure relates to the stream processing, and more particularly to determining clusters from the processed streams.
2. Discussion of Related Art
As the world becomes more interconnected and instrumented, there is a deluge of data coming from various software and hardware sensors in the form of continuous streams. Examples can be found in several domains, such as financial markets, telecommunications, surveillance, manufacturing, healthcare, and social networks. In all of these domains, there is an increasing need to gather, process, and analyze these data streams to extract insights as well as to detect emerging patterns and outliers. More importantly, this analysis often needs to be performed in near real-time.
Streaming data can be represented by using a graph. For example, data that is streamed (output) from a source (e.g., a first user) to a destination (e.g., a second user) can be represented as an edge in the graph, and the source and destination can be respective nodes of the edge. A group of related nodes in the graph may be referred to as a cluster. Further, the clusters may represent particular relationships that can be used for marketing purposes. However, since new data is constantly being input, the graph can become quite complex and it can be difficult to determine the clusters, especially when the clusters change dynamically.
Accordingly, there is a need for methods and systems that can more efficiently analyze streaming graphs.